PROJECT SUMMARY/ABSTRACT Our objective is to use HCP protocols to acquire and make public a large dataset of imaging, behavioral, and symptom data from patients with disordered emotional states. We will also develop and make public new methods for examining how connectome disorganization gives rise to these disordered states at the level of the individual patient. Psychopathology arising from enhanced negative emotion or from the loss of positive emotional experience affects over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories and are compounded by accompanying disruptions in cognitive function. Not surprisingly, therefore, these forms of psychopathology are a leading cause of disability. To address these issues our investigative strategy is informed by the Research Domain Criteria (RDoC) initiative spearheaded by NIMH. We focus on three RDoC domains and constructs: 1) acute threat within the Negative Valence System (NVS) domain, a construct relevant to automatic reactions to fear and physical symptoms of anxiety; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain, a construct involving incentive salience, hedonic responses and symptoms of anhedonia; and 3) working memory within the Cognitive System (CS) domain, a construct that implicates top-down regulation of cognitive rumination and worry. Our approach is grounded in strict adherence to HPC protocols and a strong commitment to data sharing. We unite complementary expertise, including (1) state-of-the-art MRI technology and data management systems; (2) a field-leading Center for Reproducible Neuroscience; (3) a track record in leading large-scale neuroradiology consortia; (4) leaders in RDoC-informed approaches to large-scale imaging in depression and anxiety; and (5) pioneering statistical approaches for high-dimensional data. Our aims are to (1) use the HCP protocols to acquire multi-modal data for 300 people aged 22-25 years of age who are experiencing varying degrees of acute threat, loss of reward valuation/responsiveness, and difficulties in working memory, (2) elucidate the nature of the relations among connectomes, symptoms, and behavior based on networks related to the RDoC constructs of interest, and (3) to develop data-driven, machine-learning methods to discover how connectomes for these constructs combine together to form naturally organized clusters of people. Our data will advance a neurobiological model that maps network dysfunctions to specific behaviors and symptoms. This model will provide a foundation for ultimately guiding more classifications and treatment choices according to types of neural dysfunction rather than relying on diagnostic categories that are agnostic to neurobiology.